基于EMD-模糊熵和集成学习的电动汽车充电需求预测

王毅; 谷亿; 丁壮; 李松浓; 万毅; 胡晓锐

doi:10.7500/AEPS20190424013

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基于EMD-模糊熵和集成学习的电动汽车充电需求预测

学术研究 | 更新时间：2025-06-16

- 基于EMD-模糊熵和集成学习的电动汽车充电需求预测
- Charging Demand Forecasting of Electric Vehicle Based on Empirical Mode Decomposition-Fuzzy Entropy and Ensemble Learning
- 电力系统自动化-方正平台 2020年44卷第3期页码：114-121
- 作者机构：
  
  1. 重庆邮电大学通信与信息工程学院，重庆市 400065
  2. 国网重庆市电力公司电力科学研究院，重庆市 400014
  3. 国网重庆市电力公司博士后科研工作站，重庆市 400014
- 作者简介：
  
  王毅(1981—)，男，通信作者，博士，副教授，主要研究方向：宽带电力线载波通信、智能电网用电信息采集系统。E-mail：wangyi81@cqupt.edu.cn
  谷亿(1994—)，男，硕士研究生，主要研究方向：电力数据挖掘。E-mail：ygshell1028@163.com
  丁壮(1993—)，男，硕士研究生，主要研究方向：电力数据挖掘。E-mail：dingz2619@foxmail.com
- 基金信息：
  
  重庆市自然科学基金资助项目(cstc2016jcyjA0214)
- DOI：10.7500/AEPS20190424013
  中图分类号：
- 收稿日期：2019-04-24，
  
  修回日期：2019-09-09，
  
  网络出版日期：2019-12-17，
  
  纸质出版日期：2020-03-25
- 稿件说明：
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王毅,谷亿,丁壮等.基于EMD-模糊熵和集成学习的电动汽车充电需求预测[J].电力系统自动化,2020,44(03):114-121.

WANG Yi,GU Yi,DING Zhuang,et al.Charging Demand Forecasting of Electric Vehicle Based on Empirical Mode Decomposition-Fuzzy Entropy and Ensemble Learning[J].Automation of Electric Power Systems,2020,44(03):114-121.
王毅,谷亿,丁壮等.基于EMD-模糊熵和集成学习的电动汽车充电需求预测[J].电力系统自动化,2020,44(03):114-121. DOI： 10.7500/AEPS20190424013.

WANG Yi,GU Yi,DING Zhuang,et al.Charging Demand Forecasting of Electric Vehicle Based on Empirical Mode Decomposition-Fuzzy Entropy and Ensemble Learning[J].Automation of Electric Power Systems,2020,44(03):114-121. DOI： 10.7500/AEPS20190424013.

摘要

提出一种基于经验模态分解-模糊熵和集成学习的电动汽车充电需求预测方法。该方法通过经验模态分解将电动汽车充电需求时间序列分解成相对简单的分量。为了避免分量数量过多导致计算繁琐和误差累积，首先利用模糊熵计算各分量的复杂度，并对分量进行叠加合并得到一系列子序列，减少分量数量；然后对不同频率的子序列，分别使用长短期记忆神经网络和支持向量机作为基学习器进行预测；最后采用Stacking集成学习策略，将基学习器预测结果与天气数据和分解前的充电需求时间序列数据组成特征集，经过一个全连接神经网络的学习得到最终预测结果。基于中国西南某城市中某一区域的电动汽车充电需求真实数据进行单步和多步预测实验，并与其他算法进行了对比，证明了所提方法的可靠性。

Abstract

A charging demand forecasting method of electric vehicle based on empirical mode decomposition-fuzzy entropy and ensemble learning is proposed. This method decomposes the time series of charging demand for electric vehicle into relatively simple components by empirical mode decomposition. In order to avoid the cumbersome calculation and error accumulation caused by excessive components

firstly

the complexity of each component is calculated by using fuzzy entropy. The components are superimposed and combined to obtain a series of sub-sequences to reduce the number of components. Secondly

long short-term memory (LSTM) neural networks and supported vector regression (SVR) are used as the base learner for prediction of sub-sequences with different frequencies. Finally

the prediction result of base learner

the weather data and time series data of the pre-decomposed charging demand are combined to form the feature set by the Stacking integrated learning strategy. Final forecasting results are obtained through a fully connected neural network. Single-step and multi-step prediction experiments are carried out based on real data of charging demand for electric vehicle in a certain area of a certain city in Southwest China

and the comparison with other algorithms is made， which shows the reliability of the proposed method..

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references

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